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Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm

Author

Listed:
  • Germain Lefebvre

    (Laboratoire d’Économie Mathématique et de Microéconomie Appliquée, Université Panthéon-Assas, 75006 Paris, France; Laboratoire de Neurosciences Cognitives, Institut National de la Santé et de la Recherche Médicale, 75005 Paris, France; Département d’Études Cognitives, Ecole Normale Supérieure, 75005 Paris, France)

  • Aurélien Nioche

    (Institut Jean-Nicod, Ecole Normale Supérieure, 75005 Paris, France)

  • Sacha Bourgeois-Gironde

    (Institut Jean-Nicod, Ecole Normale Supérieure, 75005 Paris, France)

  • Stefano Palminteri

    (Laboratoire de Neurosciences Cognitives, Institut National de la Santé et de la Recherche Médicale, 75005 Paris, France; Département d’Études Cognitives, Ecole Normale Supérieure, 75005 Paris, France; Institut d’Étude de la Cognition, Université de Recherche Paris Sciences et Lettres, 75005 Paris, France)

Abstract

Money is a fundamental and ubiquitous institution in modern economies. However, the question of its emergence remains a central one for economists. The monetary search-theoretic approach studies the conditions under which commodity money emerges as a solution to override frictions inherent to interindividual exchanges in a decentralized economy. Although among these conditions, agents’ rationality is classically essential and a prerequisite to any theoretical monetary equilibrium, human subjects often fail to adopt optimal strategies in tasks implementing a search-theoretic paradigm when these strategies are speculative, i.e., involve the use of a costly medium of exchange to increase the probability of subsequent and successful trades. In the present work, we hypothesize that implementing such speculative behaviors relies on reinforcement learning instead of lifetime utility calculations, as supposed by classical economic theory. To test this hypothesis, we operationalized the Kiyotaki and Wright paradigm of money emergence in a multistep exchange task and fitted behavioral data regarding human subjects performing this task with two reinforcement learning models. Each of them implements a distinct cognitive hypothesis regarding the weight of future or counterfactual rewards in current decisions. We found that both models outperformed theoretical predictions about subjects’ behaviors regarding the implementation of speculative strategies and that the latter relies on the degree of the opportunity costs consideration in the learning process. Speculating about the marketability advantage of money thus seems to depend on mental simulations of counterfactual events that agents are performing in exchange situations.

Suggested Citation

  • Germain Lefebvre & Aurélien Nioche & Sacha Bourgeois-Gironde & Stefano Palminteri, 2018. "Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 11446-11454, December.
  • Handle: RePEc:nas:journl:v:115:y:2018:p:e11446-e11454
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    Cited by:

    1. Aurélien Nioche & Basile Garcia & Germain Lefebvre & Thomas Boraud & Nicolas P. Rougier & Sacha Bourgeois-Gironde, 2019. "Coordination over a unique medium of exchange under information scarcity," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-11, December.
    2. Sacha Bourgeois-Gironde & Marcin Czupryna, 2021. "On the Extension of the Kiyotaki and Wright model to Transformable Goods," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 989-1014, April.
    3. Servet, Jean-Michel & Alary, Pierre & Desmedt, Ludovic, 2019. "Entretien avec Jean-Michel Servet," Revue de la Régulation - Capitalisme, institutions, pouvoirs, Association Recherche et Régulation, vol. 26.

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